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- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Kanezaki Asako Nitta Katsumi Tomii Norio Miyazaki Kei Okumura Keiji Sakuma Jun Miyake Yoshihiro Ono Isao Felnando Paulo Leonychev Iurii Mendieta Erick Hoa Le Cai Sean Kobayashi Yoshiyuki
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Wed7-8(M-B07（H101）)
- Group
- -
- Course number
- DSA.P412
- Credits
- 1
- Academic year
- 2024
- Offered quarter
- 1Q
- Syllabus updated
- 2024/3/29
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
The purpose of this class course is to understand the current status and state-of-the-art of social implementation of AI and data science technologies, and to examine the applicability and challenges of these technologies. In each class, lecturers from companies in various fields such as architecture, IT, finance, and materials will introduce case studies of technology and product development using data science and AI.
The goal is for students to gain a broad perspective of the real world by acquiring knowledge about the application of data science and AI technologies in a wide range of fields, and by explaining their considerations about social applications in their assigned reports.
Therefore, in addition to the seven class sessions, this course emphasizes dialogue with company lecturers, and in principle, students shall participate in the DS&AI Forum to be held face-to-face on the Ookayama campus in the afternoon of June 3, 2024. (Added on March 29, 2024)
Student learning outcomes
This course aims to develop ability of each student to be more successful in the real world with the consideration of social implementation of data science and artificial intelligence.
Course taught by instructors with work experience
| ✔ Applicable | How instructors' work experience benefits the course |
|---|---|
| In this course, lecturers from Rakuten Group, Daiichi-Sankyo, Sony will lecture on problem-solving techniques based on their practical experience. |
Keywords
Data Science, Artificial Intelligence, Machine Learning, Pharmaceutical manufacture, IT
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
This course is a live class by Zoom.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Efficient LLM Production Deployments | This lecture will provide practical recommendations for building software products with one’s own large language models (LLM). |
| Class 2 | Tips and Tricks for Building Large Scale Web Services | • Key Concepts About Web Scalability • Internet Business Trends • Common Terminology for Distributed Architectures • Dynamics of Growth • Scalable Design: High Traffic, Distributed Data • How to Prepare Organizations for Growth |
| Class 3 | Search and Ad optimization with semantic search in e-commerce domain | AI is recently booming for business applications, in this class we will learn about the process of building and implementing AI for Search and Ad optimization with emphasis in Semantic search, we will discuss the necessity and application of this type of models in the e-commerce domain. |
| Class 4 | Data science tools and advanced analytics in clinical trial operations | Descriptive analytics - example: study finder dashboard to evaluate historical clinical trial performance Predictive analytics - example: enrollment forecasting tool to predict and simulate a future study Prescriptive analytics - example: country selection tool to optimize study outcomes |
| Class 5 | Opportunities and Challenges in the Use of Real-World Evidence and AI/ML in the Drug Research and Development | Real-world data (RWD) are extensively and increasingly used in the pharmaceutical industry to drive new insights and accelerate the product research and development. RWD sources, real-world evidence (RWE), advanced methods (e.g., propensity scores, traditional and generative AI/ML), applications (e.g., to inform clinical trial design and to serve as external control arms to support regulatory decision-making for single-arm clinical trials), best practices, and case studies will be discussed. |
| Class 6 | Promotion of utilization and application examples of deep learning in Sony ＃1 | Learn about the current state of AI from a corporate perspective. |
| Class 7 | Promotion of utilization and application examples of deep learning in Sony ＃2 | Learn about examples of corporate efforts toward the AI era. |
Out-of-Class Study Time (Preparation and Review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
Textbook(s)
None required.
Reference books, course materials, etc.
Materials will be provided on T2SCHOLA in advance.
Assessment criteria and methods
No final exam will be given. The evaluation will be based on the reports of each assignment.
The evaluation will also include the results of participation in the DSAI Forum to be held on June 3,
2024. (Added on March 29, 2024)
Related courses
- XCO.T487 ： Fundamentals of data science
- XCO.T488 ： Exercises in fundamentals of data science
- XCO.T489 ： Fundamentals of artificial intelligence
- XCO.T490 ： Exercises in fundamentals of artificial intelligence
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Doctoral students must take DSA.P612 "Progressive Applied Practical Data Science and AI 1B".
Contact information (e-mail and phone) Notice : Please replace from "[at]" to "@"(half-width character).
Asako Kanezaki, Katsumi Nitta, Norio Tomii
lecture_ap[at]dsai.titech.ac.jp
Office hours
Contact by e-mail in advance to schedule an appointment.
Other
・This class is a technical course that can be considered an entrepreneurship course. The GAs that this subject corresponds to are GA0M and GA1M (added March 29, 2024).
・This course corresponds to Applied AI and Data Science C2 (XCO.T485-2), which was offered until FY2023. Students who had Applied AI and Data Science C2 as undergraduates should register for this course. Students who have taken Applied AI and Data Science C2 in graduate school may not take this course.
